Memory formation is often prevented when one event follows too soon after an initial learning event. It is also true that memory of initial learning events can be blocked if you try to learn two things at once. In fact, learning may be disrupted for both things.
In a recent test of this phenomenon, a group of 29 people (17 to 30 years of age) was trained to discriminate two sound pips that differed in length by a fraction of a second. In one group of subjects, the training occurred consecutively, which ordinarily produces some inefficiency with learning because the second task interferes with remembering the first. In this study, some learning did occur, in spite of the sequential tasks. However, results from another group of subjects revealed that when practice on the two tasks was interleaved, there was no learning on either condition.
This indicates that acquistion (initial learning) is vulnerable to multi-tasking, perhaps even more so than when learning of one task is followed too soon by another learning task. In other words, multi-tasking can interfere with initial learning, just as it does with formation of memory.
Source: Banai, K. et al. 2010. Learning two things at once: differential constraints on the acquisition and consolidation of perceptual learning. Neuroscience. 165: 436-444.
Showing posts with label memory. Show all posts
Showing posts with label memory. Show all posts
Sunday, March 7, 2010
Monday, February 22, 2010
More Evidence that Naps Help Memory
I have mentioned before the value of naps for improving the formation of memories. Another recent student confirms this conclusion. Matthew Walker and colleagues an the University of California at Berkeley divided 39 young adults into two groups. At noon, all the participants took part in a memory exercise that required them to remember faces and link them with names. Then the researchers took part in another memory exercise at 6 p.m., after 20 had napped for 100 minutes during the break.
Those who remained awake performed about 10 percent worse on the tests than those who napped, Walker said. Students take note: 10% can be the difference between an A and a B.
Source: Walker, Mathew. 2010. Current Models of Mechanisms of Sleep-Dependent Memory Presentation at the annual meeting of the American Association for the Advancement of Science meeting, San Diego, Feb. 21.
Those who remained awake performed about 10 percent worse on the tests than those who napped, Walker said. Students take note: 10% can be the difference between an A and a B.
Source: Walker, Mathew. 2010. Current Models of Mechanisms of Sleep-Dependent Memory Presentation at the annual meeting of the American Association for the Advancement of Science meeting, San Diego, Feb. 21.
Thursday, February 11, 2010
More on the Benefits of Blueberries
In several earlier posts, I discussed experiments that indicate blueberries can improve memory. There is a more recent study in 9 older adults (average age was 76) who were showing early signs of deterioration in memory capability. For 12 weeks, the subjects were given daily doses approximately 2.5 cups (exact amount adjusted according to body weight) of juice made from commercially available frozen wild blueberries. Berries were thawed, pressed, filtered, pasteurized, and then bottled. A comparison group drank the same amount of fake blueberry juice.The subjects were instructed to refrigerate the juice at home and to take prescribed daily quantities in equal, divided dosages with the morning, midday, and evening meals. Memory tests (word-pair association, word list) were given before and after the test period. Significant gains in memory ability were seen in the blueberry group. Scores on both kinds of memory tests increased about 33%.
Additionally, there was suggestive evidence that blueberry juice reduced signs of depression and lowered blood glucose levels. This needs to be pursued in future research.
In an earlier post I had summarized a study that showed that milk protein interfered with the blueberry effect. Presumably other proteins could also interfere. In other words, I am suggesting that even better results might be obtained if the juice is taken on an empty stomach (assuming of course that this does not cause upset stomach).
The beneficial effects of the blueberries are thought to be linked to their flavonoid content - in particular anthocyanins and flavanols. The exact way in which flavonoids affect the brain are unknown, but they have previously been shown to cross the blood brain barrier after dietary intake.
Source:
Krikorian, R. et al. (2010) Blueberry supplementation improves memory in older adults. J. Agricultural and Food Chemistry. doi: 10.1021/jf9029332
Friday, January 15, 2010
Learning Versus Memory
Versus? Learning and memory are different, but like two sides of the same coin. What is the difference? Learning is the acquiring of new information or skills. Memory is the remembering of what was learned. You can’t have memory without learning. You can, of course, have learning that you forget.
Learning involves at least four major processes. It all begins with registering new information. This is the stage when information is detected and encoded in brain. Paying attention obviously facilitates the registration process. Multi-tasking can create an information overload in which much of the information never gets registered. Example: a car driver who is all wrapped up in a cell phone conversation may not realize she just ran a stop sign or cut off the driver behind her in the next lane. Another example comes with reading. Reading comprehension (learning) depends heavily on the eyes actually seeing each cluster of words. The reader needs to focus on words, not letters, and needs to think about what the words mean. Likewise in images, what you learn from an image depends on the details in it that you actually notice and think about.
Next is integration. The brain likes to classify, categorize, and organize its information. Thus, new information has to be fitted into existing learned schema. This is the stage where associations are made with existing memory. Brains are really good at detecting and constructing relationships. If a given relationship is not immediately obvious, the brain may figure it out and remember it. Constructing such relationships is an integral part of the learning process.
Associations can be constructed subconsciously. If two things happen at the same time or go together in some other way, even the simplest of brains can learn the association. Moreover, cueing of relationships can produce what is called conditioned learning. We all have heard about Pavlov’s dogs. But even animals as primitive as flatworms can exhibit conditioned learning. If worms are shown flashes of light, not much happens. If they are given mild electrical shocks to the body, the body contracts. If then a flash of light is delivered just prior to an electrical shock, after enough repetitions, the worm starts contracting when it first detects the light, before any electrical shock is delivered.
Associations are still more powerful when they are consciously constructed. This is the stage where you ask yourself such questions as: Where does this information fit with what I already know? How does this relate to other things I could learn about? What value do I place on this information? How invested in using or remembering it should I be?
Then there is understanding. You can, as I did, pass college calculus by using the right formulas for given problem types, and yet not really understand what is going on with the equations. To understand, you need to answer such questions as: Is this consistent with what I thought I knew? What is missing or still confusing? What can I do with this information? What else does it appoly to, how can it be extended? What is predictable?
Learning is not complete without understanding. Understanding also creates a basis for generate insights and creative syntheses, and these in turn advance the depth and rigor of the original learning. Insights typically come from deduction or induction. Deduction is the Sherlock Holmes process of using one fact or observation to lead logically to another. Induction is the Charles Darwin process of using multiple, apparently unrelated, facts or observations to make a synthesis that accommodates them all.
Finally, there is learning to learn. This is the process of learning the paradigm, the “rules of the game,” that allows you to transfer one learned capability to new learning situations that are related. At this point, one has reached a threshold where the more you know, the more you can know.
One of the first experimental demonstrations of this phenomenon was by H. C. Blodgett in 1929. He studied maze behavior in rats, scoring how many errors they made in running the maze to find the location where a food reward was placed. Rats ran the maze once per day on successive days. The control group ran the maze and found the food, with number of errors decreasing slowly on successive days as they learned where in the maze the food was. Experimental groups ran the maze daily for three or seven days without any food reward. Naturally, they made many errors because there was nothing to learn. However, when they subsequently were allowed access to a food reward, the number of errors dropped precipitously on the very next day’s trial. In other words, the rats had been learning about the maze, its layout, number of turns, etc. during the initial explorations when no reward was available.
Blodgett called this “latent learning,” an idea expanded and formalized some 20 years later in the “Learning Set” theory of Harry Harlow. Harlow studied visual discrimination learning in monkeys and observed that visual and other types of discrimination problems progressed more quickly as a function of training on a series of different, but related problems.
These discoveries were born of necessity, arising from the need to use the same monkeys over and over in a wide variety of experiments because the Harlow lab was so under-funded. Increasing the number of problems on which monkeys were tested led to the observation that the monkeys’ general learning competence improved over time. This of course parallels the general common experience of maturation of children.
Harlow developed the prominent theory that learning any task is associated with implicit learning capabilities that can generalize to other related learning situations. The concept relates simpler trial-and-error learning to more advanced insightful-like learning, which he regarded as a mental ability that depended heavily on prior learning sets. Ability to form learning sets varies with species. Monkeys do it better than dogs or cats, and humans do it best of all.The reasons for human superiority in learning no doubt include the rich connections among various brain areas that can support and integrate more learned associations.
Learning involves at least four major processes. It all begins with registering new information. This is the stage when information is detected and encoded in brain. Paying attention obviously facilitates the registration process. Multi-tasking can create an information overload in which much of the information never gets registered. Example: a car driver who is all wrapped up in a cell phone conversation may not realize she just ran a stop sign or cut off the driver behind her in the next lane. Another example comes with reading. Reading comprehension (learning) depends heavily on the eyes actually seeing each cluster of words. The reader needs to focus on words, not letters, and needs to think about what the words mean. Likewise in images, what you learn from an image depends on the details in it that you actually notice and think about.
Next is integration. The brain likes to classify, categorize, and organize its information. Thus, new information has to be fitted into existing learned schema. This is the stage where associations are made with existing memory. Brains are really good at detecting and constructing relationships. If a given relationship is not immediately obvious, the brain may figure it out and remember it. Constructing such relationships is an integral part of the learning process.
Associations can be constructed subconsciously. If two things happen at the same time or go together in some other way, even the simplest of brains can learn the association. Moreover, cueing of relationships can produce what is called conditioned learning. We all have heard about Pavlov’s dogs. But even animals as primitive as flatworms can exhibit conditioned learning. If worms are shown flashes of light, not much happens. If they are given mild electrical shocks to the body, the body contracts. If then a flash of light is delivered just prior to an electrical shock, after enough repetitions, the worm starts contracting when it first detects the light, before any electrical shock is delivered.
Associations are still more powerful when they are consciously constructed. This is the stage where you ask yourself such questions as: Where does this information fit with what I already know? How does this relate to other things I could learn about? What value do I place on this information? How invested in using or remembering it should I be?
Then there is understanding. You can, as I did, pass college calculus by using the right formulas for given problem types, and yet not really understand what is going on with the equations. To understand, you need to answer such questions as: Is this consistent with what I thought I knew? What is missing or still confusing? What can I do with this information? What else does it appoly to, how can it be extended? What is predictable?
Learning is not complete without understanding. Understanding also creates a basis for generate insights and creative syntheses, and these in turn advance the depth and rigor of the original learning. Insights typically come from deduction or induction. Deduction is the Sherlock Holmes process of using one fact or observation to lead logically to another. Induction is the Charles Darwin process of using multiple, apparently unrelated, facts or observations to make a synthesis that accommodates them all.
Finally, there is learning to learn. This is the process of learning the paradigm, the “rules of the game,” that allows you to transfer one learned capability to new learning situations that are related. At this point, one has reached a threshold where the more you know, the more you can know.
One of the first experimental demonstrations of this phenomenon was by H. C. Blodgett in 1929. He studied maze behavior in rats, scoring how many errors they made in running the maze to find the location where a food reward was placed. Rats ran the maze once per day on successive days. The control group ran the maze and found the food, with number of errors decreasing slowly on successive days as they learned where in the maze the food was. Experimental groups ran the maze daily for three or seven days without any food reward. Naturally, they made many errors because there was nothing to learn. However, when they subsequently were allowed access to a food reward, the number of errors dropped precipitously on the very next day’s trial. In other words, the rats had been learning about the maze, its layout, number of turns, etc. during the initial explorations when no reward was available.
Blodgett called this “latent learning,” an idea expanded and formalized some 20 years later in the “Learning Set” theory of Harry Harlow. Harlow studied visual discrimination learning in monkeys and observed that visual and other types of discrimination problems progressed more quickly as a function of training on a series of different, but related problems.
These discoveries were born of necessity, arising from the need to use the same monkeys over and over in a wide variety of experiments because the Harlow lab was so under-funded. Increasing the number of problems on which monkeys were tested led to the observation that the monkeys’ general learning competence improved over time. This of course parallels the general common experience of maturation of children.
Harlow developed the prominent theory that learning any task is associated with implicit learning capabilities that can generalize to other related learning situations. The concept relates simpler trial-and-error learning to more advanced insightful-like learning, which he regarded as a mental ability that depended heavily on prior learning sets. Ability to form learning sets varies with species. Monkeys do it better than dogs or cats, and humans do it best of all.The reasons for human superiority in learning no doubt include the rich connections among various brain areas that can support and integrate more learned associations.
Thursday, October 15, 2009
Multi-tasking May Damage the Brain
We older adults tend to be awed at how young people today can multi-task. They seem to text message on cells phones, watch TV, listen to music, play a video or computer game, carrying on a conversation, and maybe even study their school lessons all simultaneously with apparent ease. Many adults, and even teachers, encourage multi-tasking because they think it is good stimulus for the brain and that learning how to multi-task is a useful skill. But I have already identified many research reports that show multi-tasking to impair formation of memory. Multi-tasking prevents the focused attention and reduction of distractions that are necessary for good memory.
Now there is a research report suggesting that the brain itself may be damaged by multi-tasking. Investigators at Stanford University gave questionnaires to their subjects to identify how much multi-tasking each person did. Nineteen subjects were "heavy multi-taskers" and 22 were "light multi-taskers." Comparison of how these two groups in thinking control tasks revealed that heavy media multi-taskers were more susceptible to interference from irrelevant environmental stimuli and from irrelevant representations in memory.In other words, they were more distractible. Then researchers tested the subjects for ability to filter relevant information from the environment and from their memories and to switch thinking tasks. A typical filtering test, for example, required subjects to detect changes in red triangles on a screen while ignoring blue triangles in the same pictures.
The heavy multi-taskers performed worse, even though their experience and presumed skill at multi-tasking should have made them more effective at these tasks. The heavy multi-taskers believed that they were good at multi-tasking, when in fact they were bad at every task that required multi-tasking.
It is not clear how much physical deterioration has occurred in brain from chronic multi-tasking. But at a minimum, multi-tasking is likely to reduce the brain's ability to develop concentration and thinking skills. Why do I suggest diminished thinking skills? Thinking is done with an orderly progression of items in working memory. Multi-tasking bombards working memory with scrambled and unfocused information and probably keeps the brain from learning how to optimize focus and orderly sequencing of thoughts through what I call the brain's "thought engine."
Source: Ophir, E., Nass, C. and Wagner, A. D. 2009. Cognitive control in media multitaskers. Procedings of the National Academy of Science. Aug. 24. doi: 10.1073/pnas0903620106
Now there is a research report suggesting that the brain itself may be damaged by multi-tasking. Investigators at Stanford University gave questionnaires to their subjects to identify how much multi-tasking each person did. Nineteen subjects were "heavy multi-taskers" and 22 were "light multi-taskers." Comparison of how these two groups in thinking control tasks revealed that heavy media multi-taskers were more susceptible to interference from irrelevant environmental stimuli and from irrelevant representations in memory.In other words, they were more distractible. Then researchers tested the subjects for ability to filter relevant information from the environment and from their memories and to switch thinking tasks. A typical filtering test, for example, required subjects to detect changes in red triangles on a screen while ignoring blue triangles in the same pictures.
The heavy multi-taskers performed worse, even though their experience and presumed skill at multi-tasking should have made them more effective at these tasks. The heavy multi-taskers believed that they were good at multi-tasking, when in fact they were bad at every task that required multi-tasking.
It is not clear how much physical deterioration has occurred in brain from chronic multi-tasking. But at a minimum, multi-tasking is likely to reduce the brain's ability to develop concentration and thinking skills. Why do I suggest diminished thinking skills? Thinking is done with an orderly progression of items in working memory. Multi-tasking bombards working memory with scrambled and unfocused information and probably keeps the brain from learning how to optimize focus and orderly sequencing of thoughts through what I call the brain's "thought engine."
Source: Ophir, E., Nass, C. and Wagner, A. D. 2009. Cognitive control in media multitaskers. Procedings of the National Academy of Science. Aug. 24. doi: 10.1073/pnas0903620106
Monday, August 10, 2009
Here's Why Marijuana Impairs Memory Formation
Scientists have known for some time that marijuana impairs the ability to convert short-term or working memories into lasting form. Now they know why. The protein synthesis machinery in the hippocampus is necessary to accomplish lasting memory formation, and a study of mouse hippocampus revealed that marijuana impairs the protein synthesis pathway responsible for memory consolidation.
Source:
Puighermannal, E. et al. 2009. Cannabinoid modulation of hippocampal long-term memory is mediated by mTOR signaling. Nature Neuroscience. On-line edition, Aug. 2; doi:10.1038/nn.2369
Source:
Puighermannal, E. et al. 2009. Cannabinoid modulation of hippocampal long-term memory is mediated by mTOR signaling. Nature Neuroscience. On-line edition, Aug. 2; doi:10.1038/nn.2369
Wednesday, April 22, 2009
Three Ways to Slow Brain Aging
Moderate physical exercise, dietary restriction, and enriched environment stimulation are all known to be good for the brain in general and memory in particular. However, few studies have directly compared these three factors all in the same study, as has been done in the lab of Alois Strasser in the University of Veterinary Medicine in Austria. Moreover, Strasser examined also a brain chemical that is likely to cause some of the brain improvement, the so-called brain-derived neurotrophic factor (BNDF), which sustains neuron life and promotes growth of neuronal processes and synapse formation.
As brain ages, the levels of BNDF typically decline. Several studies have demonstrated that BNDF is important for memory function. Research prior to that of Strasser’s lab showed that exercise “up-regulates” BNDF; that is, exercise stimulates its production. And there had been some indication that environmental enrichment (stimulation, social interactions, etc.) had a similar effect. Therefore, Strasser and colleagues examined the tissue concentrations of BDNF in the cerebral cortex of old rats.
Rats were divided randomly into six groups, living from 5 months up to 23 months. In each age group, rats were divided into those that were given free access to running wheels (RW), forced running on treadmills, food restriction, and sedentary controls with no food restriction. Rats were either either housed individually or in groups of 4 to provide social enrichment. At the end of experiments, BDNF concentrations were determined.
Researchers found higher BNDF concentrations in the 5-month-old animals than in the 23-month-old-animals, suggesting that decline in BNDF accompanies old age and probably accounts for some of the mental decline. Within the older group of rats, sedentary rats that were housed in groups had significantly higher BNDF concentration compared to the old individually caged groups. Their BNDF concentrations were even higher than those of the young baseline group. The results suggest that housing and social interactions have more influence on BDNF concentrations in the cerebral cortex of aging rats than do physical exercise and food restriction.
There was some benefit of the exercise, but only from forced running on the treadmill, not voluntary activity. However, other studies had established that even voluntary exercise by old animals increased BNDF in other parts of brain, including the area so crucial to memory formation, the hippocampus.
The lack of beneficial effect of caloric restriction in sedentary rats to weight levels matching those of the voluntary exercise group was somewhat unexpected. Prior studies in other labs had shown that such restriction does promote synaptic plasticity and even birth of new neurons. Thus, there are no doubt multiple influences that can be beneficial to brain that are not mediated by BNDF. So, to the extent that these results can be extrapolated to aging humans, it would seem like a good idea to:
- Exercise regularly and vigorously (assuming you don’t have heart trouble or other conditions that would prevent it)
- Lose weight
- Get out of the house and socialize.
Strasser, A. et al. 2006. The impact of environment in comparison with moderate physical exercise and dietary restriction on BNDF in the cerebral parietotemporal cortex of aged Sprague-Dawley rats. Gerontology. 52: 377-381.
Thursday, December 4, 2008
Make Them Learn: With Carrot or Stick?
Feedback is essential for learning. Not only does the feedback need to ensure that learning was achieved (as in testing), but feedback also needs to reinforce the motivation to learn. The age-old questions arises: do we use the carrot or the stick? Which works best, negative or positive reinforcement? Most people have an opinion, but now we have scientific studies of the question. And the answer is, it depends.
For example, three studies showed that adults correct their behavior better in response to negative feedback rather than positive feedback, whereas 8- to 11-year old children respond just the opposite. A follow-up study by Anna van Duijvenvoorde and colleagues in the
Regardless of the nature of the feedback, young adults learned bdetter than the children. For all three age groups, learning was more effective with positive feedback. Moreover, the decreased learning from negative feedback was conspicuously greater in the youngest age group, while in the young adults, the effect of feedback type was negligible.
Not surprisingly, there were brain scan indicators of differing response to type of feedback. With age, both types of feedback produced a shift toward recruiting more activity in the dorsolateral prefrontal cortex. This part of the cortex was more active after negative feed back in adults but after positive feedback in the 8-9 year-old children. The prefrontal cortex activity was about the same for negative and positive feedback in the 11-13 year olds, suggesting that this is a transition stage in development of learning style and capability.
Take home message? Positive feedback usually works best in young children (that is, after all, how they train seals). Negative feedback works just about as well as positive feedback in young adults. One more point: with the exception of language acquisition, young children are not the superior learners that many people believe.
Source: van Duijvenvoorde, A. C. K. 2008. Evaluating the negative or valuing the positive? Neural mechanisms supporting feedback-based learning across development. J. Neuroscience. 28 (38) 9495-9503.
Subscribe to:
Posts (Atom)